Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting both nationally and subnationally in the United Kingdom. These results are impacted by changes in testing effort, increases and decreases in testing effort will increase and decrease reproduction number estimates respectively.
Using data available up to the: 2020-11-24
Subnational and national estimates are available to download here.
See our Methods or our paper for an explanation of how these estimates are derived, and a pre-print for methods comparing Rt estimates by data source
| Estimate | |
|---|---|
| New confirmed cases by infection date | 9601 (5090 – 16335) |
| Expected change in daily cases | Unsure |
| Effective reproduction no. | 0.8 (0.6 – 1) |
| Rate of growth | -0.06 (-0.12 – 0) |
| Doubling/halving time (days) | -11.7 (-166.4 – -5.6) |
Figure 1: A.) Confirmed cases by date of report (bars) and their estimated date of report. B.) Confirmed cases by date of report (bars) and their estimated date of infection. C.) Time-varying estimate of the effective reproduction number (lightest ribbon = 90% credible interval; darker ribbon = the 50% credible interval, darkest ribbon = 20% credible interval). Estimates from existing data are shown up to the 2020-11-24 from when forecasts are shown. These should be considered indicative only. Estimates based on partial data have been adjusted for right truncation of infections. The vertical dashed line indicates the date of report generation. Uncertainty has been curtailed to a maximum of ten times the maximum number of reported cases for plotting purposes.
We calculate Rt as the average of how many new infections arise from one infected person. However, each data source for Covid-19 infections (test-positive cases, hospital admissions, or deaths) represents a slightly different type of “average” person who has been infected. For example, when most new infections are spread between young people who may be less vulnerable to severe disease, the Rt from all test-positive cases rises quicker than the Rt calculated from patients in hospital. This happened in the UK in August. This comparison can help us track in real-time how Covid-19 is spreading in the most vulnerable populations.
To explore in more depth how Rt from different data sources can be used to understand transmission dynamics across the population, and a more detailed methodology, see our pre-print (Sherratt et al. 2020).
Figure 2: Estimates of Rt (median, with 50% (darker shade) and 90% (lightest shade) credible interval), derived from data sources including all test-positive cases, hospital admissions, and deaths with a positive test in the previous 28 days for the devolved authorities of the United Kingdom.
Figure 3: Estimates of Rt (median, with 50% (darker shade) and 90% (lightest shade) credible interval), derived from data sources including all test-positive cases, hospital admissions, and deaths with a positive test in the previous 28 days in the NHS regions of England.
Table 2: Latest estimates of Rt, derived from data sources including all test-positive cases, hospital admissions, and deaths with a positive test in the previous 28 days. The median and 90% credible interval is shown. Latest dates vary for cases (2020-11-13), admissions (2020-11-15), and deaths (2020-11-06).
Figure 4: Confirmed cases with date of infection on the 2020-11-24 and the time-varying estimate of the effective reproduction number (lightest ribbon = 90% credible interval; darker ribbon = the 50% credible interval, darkest ribbon = 20% credible interval). Regions are ordered by the number of expected daily confirmed cases and shaded based on the expected change in daily confirmedcases. The horizontal dotted line indicates the target value of 1 for the effective reproduction no. required for control and a single case required for elimination. Uncertainty has been curtailed to a maximum of ten times the maximum number of reported cases for plotting purposes.
Figure 5: Time-varying estimate of the effective reproduction number (lightest ribbon = 90% credible interval; darker ribbon = the 50% credible interval, darkest ribbon = 20% credible interval) in the regions expected to have the highest number of new confirmed cases. Estimates from existing data are shown up to the 2020-11-24 from when forecasts are shown. These should be considered indicative only. Estimates based on partial data have been adjusted for right truncation of infections. The vertical dashed line indicates the date of report generation.
Figure 6: Confirmed cases by date of report (bars) and their estimated date of infection (lightest ribbon = 90% credible interval; darker ribbon = the 50% credible interval, darkest ribbon = 20% credible interval) in the regions expected to have the highest number of new confirmed cases. Estimates from existing data are shown up to the 2020-11-24 from when forecasts are shown. These should be considered indicative only. Estimates based on partial data have been adjusted for right truncation of infections. The vertical dashed line indicates the date of report generation. Uncertainty has been curtailed to a maximum of ten times the maximum number of reported cases for plotting purposes.
Figure 7: Confirmed cases by date of report (bars) and their estimated date of report (lightest ribbon = 90% credible interval; darker ribbon = the 50% credible interval, darkest ribbon = 20% credible interval) in the regions expected to have the highest number of new confirmed cases. Estimates from existing data are shown up to the 2020-11-24 from when forecasts are shown. These should be considered indicative only. Estimates based on partial data have been adjusted for right truncation of infections. The vertical dashed line indicates the date of report generation. Uncertainty has been curtailed to a maximum of ten times the maximum number of reported cases for plotting purposes.
Figure 8: Time-varying estimate of the effective reproduction number (lightest ribbon = 90% credible interval; darker ribbon = the 50% credible interval, darkest ribbon = 20% credible interval) in all regions. Estimates from existing data are shown up to the 2020-11-24 from when forecasts are shown. These should be considered indicative only. Estimates based on partial data have been adjusted for right truncation of infections. The horizontal dotted line indicates the target value of 1 for the effective reproduction no. required for control. The vertical dashed line indicates the date of report generation.
Figure 9: Confirmed cases by date of report (bars) and their estimated date of infection (lightest ribbon = 90% credible interval; darker ribbon = the 50% credible interval, darkest ribbon = 20% credible interval) in all regions. Estimates from existing data are shown up to the 2020-11-24 from when forecasts are shown. These should be considered indicative only. Estimates based on partial data have been adjusted for right truncation of infections. The vertical dashed line indicates the date of report generation. Uncertainty has been curtailed to a maximum of ten times the maximum number of reported cases for plotting purposes.
Figure 10: Confirmed cases by date of report (bars) and their estimated date of report (lightest ribbon = 90% credible interval; darker ribbon = the 50% credible interval, darkest ribbon = 20% credible interval) in all regions. Estimates from existing data are shown up to the 2020-11-24 from when forecasts are shown. These should be considered indicative only. Estimates based on partial data have been adjusted for right truncation of infections. The vertical dashed line indicates the date of report generation. Uncertainty has been curtailed to a maximum of ten times the maximum number of reported cases for plotting purposes.
Abbott, Sam, Katharine Sherratt, Jonnie Bevan, Hamish Gibbs, Joel Hellewell, James Munday, Patrick Barks, Paul Campbell, Flavio Finger, and Sebastian Funk. 2020. “Covidregionaldata: Subnational Data for the Covid-19 Outbreak.” - - (-): –. https://doi.org/10.5281/zenodo.3957539.
Sherratt, Katharine, Sam Abbott, Sophie R Meakin, Joel Hellewell, James D Munday, Nikos Bosse, Mark Jit, and Sebastian Funk. 2020. “Evaluating the Use of the Reproduction Number as an Epidemiological Tool, Using Spatio-Temporal Trends of the Covid-19 Outbreak in England.” medRxiv. https://doi.org/10.1101/2020.10.18.20214585.
Xu, Bo, Bernardo Gutierrez, Sarah Hill, Samuel Scarpino, Alyssa Loskill, Jessie Wu, Kara Sewalk, et al. n.d. “Epidemiological Data from the nCoV-2019 Outbreak: Early Descriptions from Publicly Available Data.” http://virological.org/t/epidemiological-data-from-the-ncov-2019-outbreak-early-descriptions-from-publicly-available-data/337.
For attribution, please cite this work as
Funk, "National and Subnational estimates for the United Kingdom", medRxiv preprint, 2020
BibTeX citation
@article{funk2020national,
author = {Funk, Katharine Sherratt*, Sam Abbott*, Sophie R Meakin, Joel Hellewell, James D Munday, Nikos Bosse, CMMID Covid-19 working group, Mark Jit, Sebastian},
title = {National and Subnational estimates for the United Kingdom},
journal = {medRxiv preprint},
year = {2020},
note = {https://www.medrxiv.org/content/10.1101/2020.10.18.20214585v1},
doi = {10.1101/2020.10.18.20214585}
}